Results for “scenario data” — Page 33

NASA NTRS2015

Retrieved Products from Simulated Hyperspectral Observations of a Hurricane

This research uses GCM derived products, with 1 km spatial resolution and sampled every 10 minutes, over a moving area following the track of a simulated severe Atlantic storm. Model products were aggregated over sounder footprints corresponding to 13 km in LEO, 2 km in LEO, and 5 km in GEO sampled every 72 minutes. We simulated radiances for instruments with AIRS-like spectral coverage, spectral resolution, and channel noise, using these aggregated products as the truth, and analyzed them using a slightly modified version of the operational AIRS Version-6 retrieval algorithm. Accuracy of retrievals obtained using simulated AIRS radiances with a 13 km footprint was similar to that obtained using real AIRS data. Spatial coverage and accuracy of retrievals are shown for all three sounding scenarios. The research demonstrates the potential significance of flying Advanced AIRS-like instruments on future LEO and GEO missions.

AIRS↗

NASA NTRS2015

Full-Scale Crash Tests and Analyses of Three High-Wing Single

The NASA Emergency Locator Transmitter Survivability and Reliability (ELTSAR) project was initiated in 2014 to assess the crash performance standards for the next generation of ELT systems. Three Cessna 172 aircraft have been acquired to conduct crash testing at NASA Langley Research Center's Landing and Impact Research Facility. Testing is scheduled for the summer of 2015 and will simulate three crash conditions; a flare to stall while emergency landing, and two controlled flight into terrain scenarios. Instrumentation and video coverage, both onboard and external, will also provide valuable data of airframe response. Full-scale finite element analyses will be performed using two separate commercial explicit solvers. Calibration and validation of the models will be based on the airframe response under these varying crash conditions.

Annett, Martin S.↗

NASA NTRS2020

Urban Air Mobility System Testbed Using CAVE Virtual Reality Environment

Urban Air Mobility (UAM) refers to a system of air passenger and small cargo transportation within an urban area. The UAM framework also includes other urban Unmanned Aerial Systems (UAS) services that will be supported by a mix of onboard, ground, piloted, and autonomous operations. Over the past few years UAM research has gained wide interest from companies and federal agencies as an on-demand innovative transportation option that can help reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. The concepts of UAM/UAS operation in the National Airspace System (NAS) remains an active area of research to ensure safe and efficient operations. With new developments in smart vehicle design and infrastructure for air traffic management, there is a need for methods to integrate and test various components of the UAM framework. In this work, we report on the development of a virtual reality (VR) testbed using the Cave Automatic Virtual Environment (CAVE) technology for human-automation teaming and airspace operation research of UAM. Using a four-wall projection system with motion capture, the CAVE provides an immersive virtual environment with real-time full body tracking capability. We created a virtual environment consisting of San Francisco city and a vertical take-off-and-landing passenger aircraft that can fly between a downtown location and the San Francisco International Airport. The aircraft can be operated autonomously or manually by a single pilot who maneuvers the aircraft using a flight control joystick. The interior of the aircraft includes a virtual cockpit display with vehicle heading, location, and speed information. The system can record simulation events and flight data for post-processing. The system parameters are customizable for different flight scenarios; hence, the CAVE VR testbed provides a flexible method for development and evaluation of UAM framework.

Marayong, Panadda↗

NASA NTRS2020

A Thousand Days After the Merger: Continued X-ray Emission from GW170817

Recent observations with the Chandra X-ray telescope continue to detect X-ray emission from the transient GW170817. In a total exposure of 96.6 ks, performed between 2020 March 9 and 16 (935–942 d after the merger), a total of 8 photons are measured at the source position, corresponding to a significance of ≈5σ. Radio monitoring with the Australian Telescope Compact Array (ATCA) shows instead that the source has faded below our detection threshold (<33 μJy, 3σ). By assuming a constant spectral index of β = 0.585, we derive an unabsorbed X-ray flux of ≈1.4 × 10−15 erg cm−2 s−1, higher than earlier predictions, yet still consistent with a simple structured jet model. We discuss possible scenarios that could account for prolonged emission in X-rays. The current data set appears consistent both with energy injection by a long-lived central engine and with the onset of a kilonova afterglow, arising from the interaction of the sub-relativistic merger ejecta with the surrounding medium. Long-term monitoring of this source will be essential to test these different models.

Gravitational waves↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

NASA NTRSDate not supplied

Towards Computational Functional Hazard Assessment (CFHA): A Gap Analysis and Concept for Emerging Aviation Systems

Given the current evolution of the National Airspace and future trajectory towards novel and evolving operations with varying levels of autonomy, complexity, and acceptable risk, there is an opportunity to support safety assurance by extending existing methodologies, such as Functional Hazard Assessment (FHA). In response to challenges in performing FHA for novel aviation concepts, we propose a concept for Computational Functional Hazard Assessment (CFHA), which provides processes, methods, and tools for incorporating external data to facilitate further exploration of the hazard space iterativelty throughout the design process. The core components of CFHA involve knowledge capture from historical and operational data, functional architecture specification via a formal modeling language, and simulation for hazardous scenario analysis. Through this concept, we aim to adapt conventional safety assessment to address the increasingly complex hazard space generated from emerging operations.

Seydou Mbaye↗

NASA NTRSDate not supplied

Towards Functional Hazard Assessment (CFHA): A Gap Analysis and Concept for Emerging Aviation Systems

Given the current evolution of the National Airspace and future trajectory towards novel and evolving operations with varying levels of autonomy, complexity, and acceptable risk, there is an opportunity to support safety assurance by extending existing methodologies, such as Functional Hazard Assessment (FHA). In response to challenges in performing FHA for novel aviation concepts, we propose a concept for Computational Functional Hazard Assessment (CFHA), which provides processes, methods, and tools for incorporating external data to facilitate further exploration of the hazard space iterativelty throughout the design process. The core components of CFHA involve knowledge capture from historical and operational data, functional architecture specification via a formal modeling language, and simulation for hazardous scenario analysis. Through this concept, we aim to adapt conventional safety assessment to address the increasingly complex hazard space generated from emerging operations.

Seydou Mbaye↗

NASA NTRS2021

Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace Gases

Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.

Muhammad Bilal↗

NASA NTRS2011

Gamma-Ray Observations of the Supernova Remnant RX J0852.0-4622 with the Fermi Large Area Telescope

We report on gamma-ray observations of the supernova remnant (SNR) RX J0852.04622 with the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope. In the Fermi-LAT data, we find a spatially extended source at the location of the SNR. The extension is consistent with the SNR size seen in other wavelengths such as X-rays and TeV gamma rays, leading to the identification of the gamma-ray source with the SNR. The spectrum is well described as a power law with a photon index of = 1.85 0.06 (stat)+0.18 0.19 (sys), which smoothly connects to the H.E.S.S. spectrum in the TeV energy band. We discuss the gamma-ray emission mechanism based on multiwavelength data. The broadband data can be fit well by a model in which the gamma rays are of hadronic origin. We also consider a scenario with inverse Compton scattering of electrons as the emission mechanism of the gamma rays. Although the leptonic model predicts a harder spectrum in the Fermi-LAT energy range, the model can fit the data considering the statistical and systematic errors.

Tanaka, T.↗

NASA NTRS1979

Inflight lightning characteristics measurement system

A research data-gathering system being developed for inflight measurement of direct and nearby lightning strike characteristics is described. Wideband analog recorders used to record the lightning scenario are supplemented with high-sample-rate digital transient recorders with augmented memory capacity for increased time resolution of specific times of interest. The endless-loop data storage technique employed by the transient recorders circumvents problems associated with oscilloscopic techniques and allows unattended operation. System integrity and immunity from induced effects is accomplished by fiber-optics signal-transmission links, shielded system enclosures, and the use of a dynamotor for power system isolation.

Pitts, F. L.↗

NASA NTRS2020

Residual Motion Estimation for Multi-Squint Airborne SAR

Airborne SAR data are often challenging to analyze due tothe limited accuracy of the platform motion measurements.In interferometric scenarios, even small residual motion er-rors cause undesirable artifacts in the interferometric phaseand geometric registration of image pairs. Various calibrationtechniques have been developed, often exploiting spectral di-versity in some way. The UAVSAR L-band system has imple-mented a new operating mode where multiple images are ac-quired simultaneously at different azimuth squint angles, pro-viding enhanced opportunities for motion calibration. Thispaper describes this imaging mode and the residual motioncalibration technique developed for these special data sets.

Hawkins, Brian↗

NASA NTRS1997

Effects of Modality on Interrupted Flight Deck Performance: Implications for Data Link

Externally-imposed tasks frequently interrupt ongoing task performance in the commercial flight deck. While normally managed without consequence, basic research as well as aviation accident and incident investigations show that interruptions can negatively affect performance and safety. This research investigates the influence of interruption and interrupted task modality on pilot performance in a simulated commercial flight deck. Fourteen current commercial airline pilots performed approach scenarios in a fixed-base flight simulator. Air traffic control instructions, conveyed either aurally or visually (via a data link system) interrupted a visual task (obtaining information from the Flight Management System) and an auditory task (listening to the automated terminal information service recording). Some results confirm the hypothesized performance advantage of cross-modality conditions, more compelling nature of auditory interruptions, and interruption-resistance of auditory ongoing tasks. However, taken together, results suggest the four interaction conditions had different effects on pilot performance. These results have implications for the design of data link systems, and for facilitating interruption management through interface design, aiding, and training programs.

Latorella, Kara A.↗

NASA NTRS2012

Siderophile Element Constraints on the Conditions of Core Formation in Mars

Siderophile element concentrations in planetary basalts and mantle samples have been used to estimate conditions of core formation for many years and have included applications to Earth, Moon, Mars and asteroid 4 Vesta [1]. For Earth, we have samples of mantle and a diverse collection of mantle melts which have provided a mature understanding of the how to reconstruct the concentration of siderophile elements in mantle materials, from only concentrations in surficial basalt (e.g., [2]). This approach has led to the consensus views that Earth underwent an early magma ocean stage to pressures of 40-50 GPa (e.g., [3,4]), Moon melted extensively and formed a small (approx. 2 mass %) metallic core [5], and 4 Vesta contains a metallic core that is approximately 18 mass % [6,7]. Based on new data from newly found meteorites, robotic spacecraft, and experimental partitioning studies, [8] showed that eight siderophile elements (Ni, Co, Mo, W, Ga, P, V and Cr) are consistent with equilibration of a 20 mass% S-rich metallic core with the mantle at pressures of 14 +/- 3 GPa. We aim to test this rather simple scenario with additional analyses of meteorites for a wide range of siderophile elements, and application of new experimental data for the volatile siderophile and highly siderophile elements.

Righter, K.↗

NASA NTRS2013

Performance Evaluation of Individual Aircraft Based Advisory Concept for Surface Management

Surface operations at airports in the US are based on tactical operations, where departure aircraft primarily queue up and wait at the departure runways. NASA's Spot And Runway Departure Advisor (SARDA) tool was developed to address these inefficiencies through Air Traffic Control Tower advisories. The SARDA system is being updated to include collaborative gate hold, either tactically or strategically. This paper presents the results of the human-in-the-loop evaluation of the tactical gate hold version of SARDA in a 360 degree simulated tower setting. The simulations were conducted for the east side of the Dallas/Fort Worth airport. The new system provides gate hold, ground controller and local controller advisories based on a single scheduler. Simulations were conducted with SARDA on and off, the off case reflecting current day operations with no gate hold. Scenarios based on medium (1.2x current levels) and heavy (1.5x current levels) traffic were explored. Data collected from the simulation was analyzed for runway usage, delay for departures and arrivals, and fuel consumption. Further, Traffic Management Initiatives were introduced for a subset of the aircraft. Results indicated that runway usage did not change with the use of SARDA, i.e., there was no loss in runway throughput as compared to baseline. Taxiing delay was significantly reduced with the use of advisory by 45% in medium scenarios and 60% in heavy. Arrival delay was unaffected by the use of advisory. Total fuel consumption was also reduced by 23% in medium traffic and 33% in heavy. TMI compliance appeared unaffected by the advisory

departure metering↗

NASA NTRS2016

Performance Evaluation of SARDA: An Individual Aircraft-Based Advisory Concept for Surface Management

Surface operations at airports in the US are based on tactical operations, where departure aircraft primarily queue up and wait at the departure runways. NASAs Spot And Runway Departure Advisor (SARDA) tool was developed to address these inefficiencies through Air Traffic Control Tower advisories. The SARDA system is being updated to include collaborative gate hold, either tactically or strategically. This paper presents the results of the human-in-the-loop evaluation of the tactical gate hold version of SARDA in a 360 degree simulated tower setting. The simulations were conducted for the east side of the Dallas-Fort Worth airport. The new system provides gate hold, ground controller and local controller advisories based on a single scheduler. Simulations were conducted with SARDA on and off, the off case reflecting current day operations with no gate hold. Scenarios based on medium (1.2x current levels) and heavy (1.5x current levels) traffic were explored. Data collected from the simulation was analyzed for runway usage, delay for departures and arrivals, and fuel consumption. Further, Traffic Management Initiatives were introduced for a subset of the aircraft. Results indicated that runway usage did not change with the use of SARDA, i.e., there was no loss in runway throughput as compared to baseline. Taxiing delay was significantly reduced with the use of advisory by 45 in medium scenarios and 60 in heavy. Arrival delay was unaffected by the use of advisory. Total fuel consumption was also reduced by 23 in medium traffic and 33 in heavy. TMI compliance appeared unaffected by the advisory.

Yoon Jung↗

NASA NTRS2011

Reynolds-Averaged Navier-Stokes Solutions to Flat Plate Film Cooling Scenarios

The predictions of several Reynolds-Averaged Navier-Stokes solutions for a baseline film cooling geometry are analyzed and compared with experimental data. The Fluent finite volume code was used to perform the computations with the realizable k-epsilon turbulence model. The film hole was angled at 35 to the crossflow with a Reynolds number of 17,400. Multiple length-to-diameter ratios (1.75 and 3.5) as well as momentum flux ratios (0.125 and 0.5) were simulated with various domains, boundary conditions, and grid refinements. The coolant to mainstream density ratio was maintained at 2.0 for all scenarios. Computational domain and boundary condition variations show the ability to reduce the computational cost as compared to previous studies. A number of grid refinement and coarsening variations are compared for further insights into the reduction of computational cost. Liberal refinement in the near hole region is valuable, especially for higher momentum jets that tend to lift-off and create a recirculating flow. A lack of proper refinement in the near hole region can severely diminish the accuracy of the solution, even in the far region. The effects of momentum ratio and hole length-to-diameter ratio are also discussed.

Johnson, Perry L.↗

NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗